Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification

Computers in Biology and Medicine

Volumn 42, Issue 6, 2012, Pages 639-650

  Urquiza, J M ^a   Rojas, I ^a   Pomares, H ^a   Herrera, J ^a   Florido, J P ^a   Valenzuela, O ^b   Cepero, M ^b  

^a Department of Computer Architecture and Computer Technology ^*  (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Feature extraction and selection; PPI classification; Proteomic in protein interaction; Support vector machines

Indexed keywords

BIOLOGICAL PROCESS; CONFIDENCE SCORE; DATA CLASSIFICATION; DATA MINING TECHNIQUES; DATA SETS; FEATURE EXTRACTION AND SELECTION; MACHINE LEARNING TECHNIQUES; PREDICTION CAPABILITY; PROTEIN INTERACTION; PROTEIN-PROTEIN INTERACTIONS; PROTEOMICS; ROC ANALYSIS; SENSITIVITY AND SPECIFICITY; SIMILARITY MEASURE; SUPPORT VECTOR;

DATABASE SYSTEMS; FEATURE EXTRACTION; FORECASTING; MOLECULAR BIOLOGY; PROTEINS; SEMANTICS; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

PROTEIN;

ACCURACY; ALGORITHM; ARTICLE; BIOINFORMATICS; CAENORHABDITIS ELEGANS; DROSOPHILA MELANOGASTER; EXTRACTION; GENOMICS; HUMAN; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN PROTEIN INTERACTION; PROTEOMICS; SACCHAROMYCES CEREVISIAE; SUPPORT VECTOR MACHINE; VALIDATION STUDY; YEAST;

DATA MINING; DATABASES, GENETIC; GENOMICS; PROTEIN INTERACTION MAPPING; PROTEOMICS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SACCHAROMYCES CEREVISIAE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES;

EID: 84861223047     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2012.01.010     Document Type: Article

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